Can StarCoder2 7B run on Mac Studio M1 Ultra 128GB?

YES — Runs Great

C46Usable

Estimated from fit model

StarCoder2 7B needs ~19.8 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.8 GB, 98.0 tok/s, Runs well

19.8 GB required92.2 GB available

21% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

1.4M

Memory

19.8 GB / 92.2 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on Mac Studio M1 Ultra 128GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	98.0 tok/s	1078 ms	1.4M
Coding	C	Runs well	98.0 tok/s	1976 ms	1.4M
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	1.4M
Reasoning	C	Runs well	98.0 tok/s	2335 ms	1.4M
RAG	C	Runs well	98.0 tok/s	3592 ms	1.4M

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4	3.9 GB	Medium	D39
Q4_K_M	4	4.3 GB	Medium	D39
Q5_K_M	5	5.0 GB	High	D39
Q6_K	6	5.7 GB	High	D39
Q8_0	8	7.5 GB	Very High	D39
F16Best for your GPU	16	14.3 GB	Maximum	D40

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Frequently asked questions

Can Mac Studio M1 Ultra 128GB run StarCoder2 7B?

Yes, Mac Studio M1 Ultra 128GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 19.8 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, StarCoder2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can Mac Studio M1 Ultra 128GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on Mac Studio M1 Ultra 128GB receives a C grade with 98.0 tok/s and 1.4M context.

What context window can StarCoder2 7B use on Mac Studio M1 Ultra 128GB?

On Mac Studio M1 Ultra 128GB, StarCoder2 7B can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for StarCoder2 7B?

Not always. Mac Studio M1 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M1 Ultra 128GB See all hardware for StarCoder2 7B

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